The influence of weather on travel behaviour - a multi-method analysis

Societal and political attention to the effects of climate change and possible mitigation and adaptationpolicies has increased sharply in the last decades, resulting partly from increasing awareness about therole of humanity and partly from the ever more noticeable changes in our world caused by climatechange. This societal interest has highlighted a lack of knowledge about the effects that a changingclimate will have on many aspects of our lifes, one of which is the transport section. To understand theimpact climate change will have on our transport system we need to know how travel behaviours areaffected by weather circumstances, which is the main topic of our research.We focus on four aspects of the relationship between weather and travel behaviour: (1) how weatheris taken into account in the decision-making process (2) if the influence of singular weather variables(such as temperature) depend on the value of other parameters (3) if the influence of weather is differentfor urban and rural areas and (4) whether there are groups of people whose response to travelbehaviour are distinctly different from one-another. This knowledge can be used for climate changeadaptation measures, such as ensuring that our supply of travel infrastructure will be able to copewith changes in travel demand resulting from a changed climate, and mitigation measures, such asincreasing the number of people that use more sustainable travel options like the bicycle.For our analyses we use travel data provided by the KiM Netherlands Institute for Transport PolicyAnalysis, which is the result from a travel diary survey held in autumn. We use weather data asmeasured by weather stations, provided by the Royal Netherlands Meteorological Institute (KNMI).This data is used to estimate the influence of weather on travel demand and mode choice in the Netherlands,using regression and choice models respectively. Within our analyses we try to find factorsthat moderate the relationship between weather and travel behaviour, such as urban density and sociodemographics.With respect to the four aspects identified above, we report the following findings:(1) that people use a general perception of the weather during the whole day for their mode choicetravel decisions, which contrasts with the most common practice of using the weather at the trips’departure time.(2) by accounting for the fact that meteorological variables always co-occur in our models we areable to more accurately capture its effect on travel behaviour. The difference with the current practiceof estimating separate effects for each weather variable is particularly stark for days at the extreme endof the observed range of weather variables.(3) The influence of weather on travel behaviour differs more qualitatively between rural and urbanareas: the total effect size of the weather similar, but they are brought upon by different weathervariables. The difference is also very specific to travel modes. For bicyclists the effects of wind speedseem to be more sizeable in urban environments, whilst temperature, rain, and sunshine have smallereffects in urban environments.(4)We find multiple groups of travellers whose responses to weather variations are different from oneanother. These differences seem to be caused by the set of travel modes that are used during averageweather conditions. People that only use the car during average conditions are not very affected, withonly enjoyable weather conditions prompting increased bicycle use. If the car and the bicycle are usedoften people swap between the modes, although use of the bicycle during inclement conditions isrelatively much higher than for the other two groups. The last group has a more multi-modal travelpattern, which results in the largest variations caused by weather. Inclement conditions favour bothpublic transport and the car, with car use increasing quite sharply during wet weather with high windspeeds.Additionally we find that weather variations account for differences in travel behaviour across boththe spatial and temporal dimensions. A particularly surprising finding is that the smaller number ofbike trips in the western provinces of the Netherlands can be fully explained by the fact that there arehigher average wind speeds and lower temperatures in this part of the country.Our results have several implications for the research community and policy makers. We advice researchersto account for the fact that the weather is perceived as a whole and thus that the effect ofone single variable (such as temperature) will depend on the values of other variables. We also foundinteresting subgroups with different reactions with regards to weather. We advice researchers to moreclosely investigate the effects of weather for the separate subgroups. Finally we find sizeable differencesin the effect of weather between different regions, even within the relatively small country of theNetherlands. Researchers studying a relatively large study area would do well to estimate separateeffects for regions within their study area, for example based on population density and geographicallocation.For policy makers our findings imply that there is a sizeable effect of weather that could be usedto improve the forecasts of future travel demand, both in the short- and long terms. Whilst policymakers obviously can’t control the weather, we have found that changing travel patterns or attitudesto travel modes will have repercussions for the effect weather has on travel behaviour. We think thatpolicies aimed at allowing commuters to gain experience with using the bicycle for their daily commuteduring summer, coupled with temporary financial incentives when weather conditions becomeless favourable, could be one way of achieving more cyclists during inclement conditions. Policy makerscould even target younger professionals specifically, as they are much more likely to have alreadydeveloped such habits during their education.Engineering and Policy Analysi

Consumer studies on digital platforms adoption and continuance: A structured literature review

As digital platforms have become the locus of competition and innovation in the digital economy, it is imperative to understand consumer decisions on adoption and usage. Digital platforms have unique traits, as they are extensible and often mediate between users. These traits may require understandings to move beyond generic (post-)adoption theories. This paper reviews the rapidly growing body of empirical consumer studies on digital platforms in order to identify emerging trends in this field. After a structured literature review, we conduct a network analysis using the hypotheses that are supported in the papers. We find a wide variety of over 130 concepts is considered. Factors from generic (post-) adoption theories are often studied, alongside factors related to the concept of network effects. We find differences between the importance of direct, indirect, local and global network effects. We find that studies pay relatively little attention to characteristics that arise from the extensibility of digital platforms, such as openness, control, security and privacy. We posit that IS researchers have the important task of opening up this black box of digital platform characteristics and theorize how notions of extensibility and generativity affect consumer decisions, going beyond mainstream theories on adoption and continuance.Information and Communication Technolog

The role of travel-related reasons for location choice in residential self-selection

Residential self-selection (RSS) is the theoretical mechanism that explains that the impact of the built environment on travel behaviour is weaker than bivariate correlations suggest, because mode attitudes influence both the built environment and travel behaviour and therefore at least partially account for the bivariate relationship. Recently, the concept of travel-related reasons for residential choice has been introduced, which reflects the actual extent to which the travel-related characteristics of the built environment were considered during the relocation decision. In this paper, we hypothesize that travel-related location reasons are stronger predictors of the built environment choice than generic mode attitudes. This hypothesis is examined by estimating both a cross-sectional and a longitudinal Structural Equation Model using data gathered in the Netherlands. The results suggest that the travel-related location reasons are indeed stronger predictors for built environment location than travel mode attitudes and that the directions of causality between attitudes, travel-related location reasons, the built environment, and travel behaviour often run in both directions. Substantively, our findings indicate that public transport use is most strongly affected by the built environment (after controlling for both stated reasons and attitudes), while car and bicycle use are hardly affected. From a practical point of view, this suggests that transforming the built environment to be more friendly to public transport may increase the use of public transport, but that, at least in the Netherlands, such a strategy would not work well if the aim were to reduce car use or increase bicycle use.Transport and Logistic